auto-save 2026-05-18 07:05 (~8)

2026-05-18 07:06:00 +08:00
parent d72bf62a97
commit 5fde9f3e22
8 changed files with 164 additions and 35 deletions
--- a/.memory/worklog.json
+++ b/.memory/worklog.json
@@ -1,18 +1,5 @@
 {
  "entries": [
-    {
-      "files_changed": 1,
-      "message": "Codex 会话活跃 · 最近命令：codex · 1 项未提交变更 · 最近提交：auto-save 2026-05-15 16:49 (~4)",
-      "ts": "2026-05-15T08:54:48Z",
-      "type": "session-heartbeat"
-    },
-    {
-      "files_changed": 1,
-      "hash": "0d57081",
-      "message": "auto-save 2026-05-15 16:55 (~1)",
-      "ts": "2026-05-15T16:55:21+08:00",
-      "type": "commit"
-    },
    {
      "files_changed": 3,
      "hash": "c53d27d",
@@ -3254,6 +3241,19 @@
      "type": "session-heartbeat",
      "message": "Codex 会话活跃 · 最近命令：codex · 分支 main · 1 项未提交变更 · 最近提交：fix: use image edits for gpt references",
      "files_changed": 1
+    },
+    {
+      "ts": "2026-05-18T07:00:37+08:00",
+      "type": "commit",
+      "message": "auto-save 2026-05-18 07:00 (~2)",
+      "hash": "d72bf62",
+      "files_changed": 2
+    },
+    {
+      "ts": "2026-05-17T23:03:44Z",
+      "type": "session-heartbeat",
+      "message": "Codex 会话活跃 · 最近命令：codex · 分支 main · 7 项未提交变更 · 最近提交：auto-save 2026-05-18 07:00 (~2)",
+      "files_changed": 7
    }
  ]
 }
--- a/RULES.md
+++ b/RULES.md
@@ -59,6 +59,7 @@
 - `REWRITE_MODEL`：通用改写/分镜描述模型，默认 `gemini-2.5-pro`
 - `AUDIO_REWRITE_MODEL`：后续音频口播改写模型，默认跟随 `REWRITE_MODEL`；当前第一步不默认调用口播改写，只保留原文案和声音分析
 - `AUDIO_PRODUCT_BRIEF`：音频口播改写时注入的 SKG 产品卖点
+- `PRODUCT_VIEW_MODEL`：同一产品素材池的视角标注/自动识别模型；当前按项目要求强制使用 `gpt-image-2`
 - `IMAGE_BASE_URL` / `IMAGE_API_KEY` / `IMAGE_MODEL`：OpenAI 兼容生图网关；当前所有生图入口一律强制使用 `gpt-image-2`，不做其他图片模型 fallback
 - `GPT_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODEL` / `SUBJECT_ASSET_IMAGE_MODELS`：保留兼容旧环境变量名，但服务端会强制主体 6 视图和所有其他生图入口都只使用 `gpt-image-2`
 - `VOICE_PROVIDER`：配音通道，当前固定使用 `azure_openai`
--- a/api/.env.example
+++ b/api/.env.example
@@ -18,6 +18,7 @@ LOCAL_ASR_MODEL=mlx-community/whisper-tiny
 LOCAL_ASR_TIMEOUT_SECONDS=180
 TRANSLATE_MODEL=gemini-2.5-flash
 REWRITE_MODEL=gemini-2.5-pro
+PRODUCT_VIEW_MODEL=gpt-image-2
 IMAGE_BASE_URL=https://ai.skg.com/ezlink/v1
 IMAGE_API_KEY=
 IMAGE_MODEL=gpt-image-2
--- a/api/main.py
+++ b/api/main.py
@@ -4624,6 +4624,8 @@ class CopyCharacterLibraryAssetReq(BaseModel):

 class GenerateProductAngleAssetReq(BaseModel):
    source_ref: dict
+    source_refs: list[dict] = Field(default_factory=list)
+    source_notes: list[str] = Field(default_factory=list)
    target_view: str
    note: str = ""

@@ -5016,7 +5018,7 @@ def product_view_batch_prompt(indices: list[int]) -> str:


 def analyze_product_view(ref_path: Path, index: int) -> dict:
-    if not LLM_API_KEY:
+    if not (IMAGE_API_KEY if PRODUCT_VIEW_MODEL == GPT_IMAGE_MODEL else LLM_API_KEY):
        return fallback_product_view(index)
    img_b64 = base64.b64encode(ref_path.read_bytes()).decode("ascii")
    prompt = (
@@ -5029,8 +5031,8 @@ def analyze_product_view(ref_path: Path, index: int) -> dict:
        "{\"view\":\"front|left_45|right_45|side_thickness|inner_contacts|back_bottom\",\"background\":\"white|black|simple|complex|unknown\",\"use_tags\":[\"hero_packshot\"],\"orientation\":{\"product_left\":\"图中哪一侧/不可见/不确定\",\"product_right\":\"图中哪一侧/不可见/不确定\",\"top\":\"图中哪一侧/不可见/不确定\",\"bottom\":\"图中哪一侧/不可见/不确定\",\"inner_side\":\"图中哪一侧/是否可见\",\"outer_side\":\"图中哪一侧/是否可见\",\"opening_direction\":\"U形开口朝图中哪一侧/不可见/不确定\"},\"landmarks\":[\"U形开口\"],\"note\":\"中文备注\",\"risk\":\"\",\"confidence\":0.86}."
    )
    try:
-        resp = llm().chat.completions.create(
-            model=VISION_MODEL,
+        resp = product_view_llm().chat.completions.create(
+            model=PRODUCT_VIEW_MODEL,
            messages=[{"role": "user", "content": [
                {"type": "text", "text": prompt},
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}},
@@ -5050,7 +5052,7 @@ def analyze_product_view(ref_path: Path, index: int) -> dict:


 def analyze_product_views_batch(paths_by_index: list[tuple[int, Path]]) -> dict[int, dict]:
-    if not LLM_API_KEY:
+    if not (IMAGE_API_KEY if PRODUCT_VIEW_MODEL == GPT_IMAGE_MODEL else LLM_API_KEY):
        return {index: fallback_product_view(index) for index, _path in paths_by_index}
    results: dict[int, dict] = {}
    for start in range(0, len(paths_by_index), PRODUCT_VIEW_BATCH_SIZE):
@@ -5062,8 +5064,8 @@ def analyze_product_views_batch(paths_by_index: list[tuple[int, Path]]) -> dict[
            content.append({"type": "text", "text": f"Image index {index}"})
            content.append({"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}})
        try:
-            resp = llm().chat.completions.create(
-                model=VISION_MODEL,
+            resp = product_view_llm().chat.completions.create(
+                model=PRODUCT_VIEW_MODEL,
                messages=[{"role": "user", "content": content}],
                response_format={"type": "json_object"},
                temperature=0.05,
@@ -5121,18 +5123,68 @@ def analyze_product_views(job_id: str, req: AnalyzeProductViewsReq) -> dict:
    return {"items": items, "missing_views": missing}


+def _make_product_angle_reference_sheet(paths: list[Path], out_path: Path) -> Path:
+    thumbs: list[Image.Image] = []
+    for path in paths[:6]:
+        try:
+            img = ImageOps.exif_transpose(Image.open(path)).convert("RGB")
+            img.thumbnail((520, 520), Image.Resampling.LANCZOS)
+            cell = Image.new("RGB", (560, 560), (255, 255, 255))
+            cell.paste(img, ((560 - img.width) // 2, (560 - img.height) // 2))
+            thumbs.append(cell)
+        except Exception:
+            continue
+    if not thumbs:
+        raise RuntimeError("no usable product reference images")
+    cols = 3 if len(thumbs) > 2 else len(thumbs)
+    rows = (len(thumbs) + cols - 1) // cols
+    sheet = Image.new("RGB", (cols * 560, rows * 560), (245, 245, 245))
+    for i, thumb in enumerate(thumbs):
+        sheet.paste(thumb, ((i % cols) * 560, (i // cols) * 560))
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    sheet.save(out_path, "JPEG", quality=94)
+    return out_path
+
+
@app.post("/jobs/{job_id}/assets/product-angle")
 def generate_product_angle_asset(job_id: str, req: GenerateProductAngleAssetReq) -> dict:
    if job_id not in JOBS:
        raise HTTPException(404, "job not found")
-    source_path = storyboard_ref_path(job_id, req.source_ref)
-    if not source_path or not source_path.exists():
+    raw_refs = [req.source_ref] + list(req.source_refs or [])
+    source_paths: list[Path] = []
+    seen_paths: set[str] = set()
+    for ref in raw_refs:
+        ref_path = storyboard_ref_path(job_id, ref)
+        if ref_path and ref_path.exists():
+            key = str(ref_path)
+            if key not in seen_paths:
+                seen_paths.add(key)
+                source_paths.append(ref_path)
+        if len(source_paths) >= 6:
+            break
+    if not source_paths:
        raise HTTPException(404, "source product image not found")
+    source_path = source_paths[0]
+    model_src = source_path
+    sheet_tmp: Path | None = None
+    if len(source_paths) > 1:
+        sheet_tmp = job_dir(job_id) / "tmp" / f"product_angle_refs_{uuid.uuid4().hex[:8]}.jpg"
+        model_src = _make_product_angle_reference_sheet(source_paths, sheet_tmp)
    target_view = (req.target_view or "目标视角").strip()
    note = (req.note or "").strip()
+    source_notes = [re.sub(r"\s+", " ", str(item)).strip()[:180] for item in (req.source_notes or []) if str(item).strip()]
+    source_note_clause = (
+        "Uploaded reference notes from the operator/view recognizer: "
+        + " | ".join(source_notes[:6])
+        + ". "
+        if source_notes
+        else ""
+    )
    prompt = (
-        "Use the reference image as the same SKG neck-and-shoulder wearable massage product. "
+        "Use the reference image or reference board as evidence for the same SKG neck-and-shoulder wearable massage product. "
+        "If a reference board is provided, all panels are the same product from uploaded views; do not output a board, collage, or multiple products. "
        f"Generate a clean product-only white-background reference image in this missing view: {target_view}. "
+        + source_note_clause
        "Preserve the exact product identity: white U-shaped wearable neck and shoulder massager that sits around the neck, asymmetric wearer-left and wearer-right details, side buttons, inner metal massage contacts, opening width, material, thickness, curvature, and real shoulder-neck wearing scale. "
        "Use product coordinates: wearer-left/right are the user's body left/right when worn, top is near chin/upper neck, bottom is near collarbone/shoulders, inner side touches skin, outer side is the shell/buttons. "
        "Do not mirror both sides into identical shapes; keep visible left/right asymmetry and believable shoulder-neck wearable proportions. "
@@ -5142,9 +5194,15 @@ def generate_product_angle_asset(job_id: str, req: GenerateProductAngleAssetReq)
    )
    models = [GPT_IMAGE_MODEL]
    try:
-        img_bytes, _mode = _image_edit_call(source_path, prompt, models=models, fallback_text=False, max_attempts=5, max_side=1280)
+        img_bytes, _mode = _image_edit_call(model_src, prompt, models=models, fallback_text=False, max_attempts=5, max_side=1600)
    except RuntimeError as e:
        raise HTTPException(_image_error_status(e), f"product angle generation failed: {e}")
+    finally:
+        if sheet_tmp and sheet_tmp.exists():
+            try:
+                sheet_tmp.unlink()
+            except OSError:
+                pass
    asset_id = f"product_angle_{uuid.uuid4().hex[:10]}"
    out_path = job_dir(job_id) / "assets" / f"{asset_id}.jpg"
    _normalize_asset_image(img_bytes, out_path, source_path, "1024", "white", square=True, fill_subject=True)
--- a/deploy/.env.production.example
+++ b/deploy/.env.production.example
@@ -23,6 +23,7 @@ ASR_MODEL=whisper-1
 ASR_FALLBACK_MODEL=gemini-2.5-flash
 TRANSLATE_MODEL=gemini-2.5-flash
 REWRITE_MODEL=gemini-2.5-pro
+PRODUCT_VIEW_MODEL=gpt-image-2
 IMAGE_BASE_URL=https://ai.skg.com/ezlink/v1
 IMAGE_API_KEY=
 IMAGE_MODEL=gpt-image-2
--- a/docs/source-analysis.html
+++ b/docs/source-analysis.html
@@ -870,7 +870,7 @@ ProductRefStateItem {
          </thead>
          <tbody>
            <tr><td>网页登录</td><td><code>POST /auth/login</code>、<code>GET /auth/check</code>、<code>POST /auth/logout</code></td><td><code>web/app/login/page.tsx</code>、Nginx <code>auth_request</code></td><td>登录页提交账号密码到 <code>/api/auth/login</code>，后端设置 HttpOnly 会话 Cookie；生产 Nginx 对工作台和 <code>/api/</code> 调 <code>/auth/check</code> 做统一校验，未登录页面跳 <code>/login/</code>，API 返回 JSON 401。</td></tr>
-            <tr><td>运行配置 / 模型标注</td><td><code>GET /health</code></td><td><code>getRuntimeHealth</code>、<code>ModelTrace</code></td><td>返回 <code>models</code>：ASR、本机 ASR、ASR fallback、翻译、改写、Vision、GPT 图像模型、主体 6 视图 GPT 图像模型、Azure OpenAI TTS、视频别名和 Seedance 服务商。前端所有当前主路径里会调用模型的按钮旁显示模型名，点击弹出小窗口查看模型链路和输入输出逻辑；不返回 API Key 或敏感凭证。</td></tr>
+            <tr><td>运行配置 / 模型标注</td><td><code>GET /health</code></td><td><code>getRuntimeHealth</code>、<code>ModelTrace</code></td><td>返回 <code>models</code>：ASR、本机 ASR、ASR fallback、翻译、改写、通用 Vision、产品视角识别 <code>product_view</code>、GPT 图像模型、主体 6 视图 GPT 图像模型、Azure OpenAI TTS、视频别名和 Seedance 服务商。前端所有当前主路径里会调用模型的按钮旁显示模型名，点击弹出小窗口查看模型链路和输入输出逻辑；不返回 API Key 或敏感凭证。</td></tr>
            <tr><td>历史列表</td><td><code>GET /jobs</code></td><td><code>listJobs</code></td><td>所有 job 精简列表（id/url/status/thumbnail/mtime…），按 state.json mtime 倒序。前端 URL 无 <code>?job=</code> 时拉它回填全部历史；带 <code>limit</code> 可截断。</td></tr>
            <tr><td>创建任务</td><td><code>POST /jobs</code></td><td><code>createJob</code></td><td>提交 TK 链接，后台开始下载；前端“开始”队列会在 downloaded 后自动触发音频解析。</td></tr>
            <tr><td>上传视频</td><td><code>POST /jobs/upload</code></td><td><code>uploadJob</code></td><td>保存 source.mp4，然后同样进入下载完成状态；当前上传后也加入第一步队列，下载完成后自动解析音频。</td></tr>
@@ -893,8 +893,8 @@ ProductRefStateItem {
            <tr><td>产品图库</td><td><code>GET /product-library/skg</code></td><td><code>listProductLibrary</code></td><td>读取内置 SKG 白底图库 manifest，返回产品标题、品类、尺寸、白底评分和预览图 URL。</td></tr>
            <tr><td>产品图入库到 job</td><td><code>POST /jobs/{id}/assets</code>、<code>POST /jobs/{id}/assets/product-library</code></td><td><code>uploadStoryboardAsset</code>、<code>copyProductLibraryAsset</code></td><td>上传产品图或把内置产品图库条目复制为当前 job 的普通 asset。后端统一生成最长边 1600px、JPEG 92 的 AI 工作副本，透明底铺白，过大/过小图片会在 <code>ImageRef.asset_meta</code> 里返回转换动作和风险；黑底/白底背景本身不强行转换。注意该接口只写图片文件，产品素材池列表另由 <code>PUT /jobs/{id}/product-refs</code> 持久化。</td></tr>
            <tr><td>产品素材池保存</td><td><code>PUT /jobs/{id}/product-refs</code></td><td><code>saveProductRefs</code></td><td>把当前 job 的产品素材池列表、识别视角、用途标签、方向、结构点、备注、AI 补图和删除结果保存到 <code>Job.product_refs</code> / <code>state.json</code>。前端上传、识别完成、补角度、编辑备注和删除时都会同步保存；刷新页面或热更新后从 job 恢复，不再要求重新上传和重新识别。</td></tr>
-            <tr><td>产品视角识别</td><td><code>POST /jobs/{id}/assets/product-views/analyze</code></td><td><code>analyzeProductViews</code></td><td>读取同一产品素材池，按批次把多张图一次性提交给视觉模型，不限制只看前 6 张；识别对象被固定为套在脖子上的 U 形肩颈按摩仪。返回 <code>view</code>、<code>background</code>、<code>use_tags</code>、<code>orientation</code>、<code>landmarks</code>、中文备注、生成风险和置信度；<code>orientation</code> 明确佩戴者左/右、上/下、内外侧和开口方向对应图中哪边，避免把图片左右误当产品左右。前端不再要求用户手动选择视角，也不做不同产品身份判断。</td></tr>
-            <tr><td>产品缺角度补图</td><td><code>POST /jobs/{id}/assets/product-angle</code></td><td><code>generateProductAngleAsset</code></td><td>用当前产品白底图作为参考，通过图像模型自动补全缺失视角，输出新的 <code>ImageRef(kind="asset")</code>。Prompt 会约束白底产品图、左右非对称、厚度、内侧触点和肩颈真实佩戴比例；图生图通过 <code>/images/edits</code> multipart 提交参考图，不再把 <code>image</code> 当 JSON 参数塞进 <code>/images/generations</code>；遇到 <code>gpt-image-2</code> 上游 429 / saturated 会按退避节奏重试，最终仍失败时返回 503 和可读提示。前端只在自动补图失败时暴露重试入口。</td></tr>
+            <tr><td>产品视角识别</td><td><code>POST /jobs/{id}/assets/product-views/analyze</code></td><td><code>analyzeProductViews</code></td><td>读取同一产品素材池，按批次把多张图一次性提交给 <code>PRODUCT_VIEW_MODEL=gpt-image-2</code> 做视角标注，不限制只看前 6 张；识别对象被固定为套在脖子上的 U 形肩颈按摩仪。返回 <code>view</code>、<code>background</code>、<code>use_tags</code>、<code>orientation</code>、<code>landmarks</code>、中文备注、生成风险和置信度；<code>orientation</code> 明确佩戴者左/右、上/下、内外侧和开口方向对应图中哪边，避免把图片左右误当产品左右。前端不再要求用户手动选择视角，也不做不同产品身份判断。</td></tr>
+            <tr><td>产品缺角度补图</td><td><code>POST /jobs/{id}/assets/product-angle</code></td><td><code>generateProductAngleAsset</code></td><td>用当前同一产品素材池作为参考，通过 <code>gpt-image-2</code> 自动补全缺失视角，输出新的 <code>ImageRef(kind="asset")</code>。前端不再固定传第一张图，而是按目标视角给已上传/已标注参考图打分，优先选择真实上传图、目标相邻视角、侧厚/触点/底部对应用途标签和低风险高置信图，最多传 6 张；后端把这些参考图拼成同产品参考板，再通过 <code>/images/edits</code> multipart 提交给 <code>gpt-image-2</code>。Prompt 会约束白底产品图、左右非对称、厚度、内侧触点和肩颈真实佩戴比例，并禁止输出拼图/多产品；遇到上游 429 / saturated 会按退避节奏重试，最终仍失败时返回 503 和可读提示。</td></tr>
            <tr><td>角色库</td><td><code>GET /character-library/skg</code></td><td><code>listCharacterLibrary</code></td><td>读取内置 5 个透明骨架人角色 manifest，每个角色含正面、左右 45 度、侧面、背面、半身近景和背部特写 7 张参考图。</td></tr>
            <tr><td>角色图入库到 job</td><td><code>POST /jobs/{id}/assets/character-library</code></td><td><code>copyCharacterLibraryAssets</code></td><td>把所选角色的 7 张参考图复制为当前 job asset，返回 <code>subject_images</code>，产品融合生成视频时作为人物身份参考图提交。</td></tr>
            <tr><td>产品融合引导图</td><td><code>POST /jobs/{id}/product-fusion/guide</code></td><td><code>createProductFusionGuide</code></td><td>旧流程兼容接口：读取产品图和白底人物图，按 <code>product_region</code> 合成位置引导图。当前内置角色 + 产品 + 描述流程不再主动调用它。</td></tr>
@@ -1004,6 +1004,19 @@ ProductRefStateItem {
        <h2>变更记录</h2>
        <p>这个记录不是 git log 的替代品。它记录“产品理解发生了什么变化、影响了哪些源码、你以后描述需求时该怎么说”。后续每次改功能都要补一条。</p>
        <div class="changelog">
+          <article class="change">
+            <header>
+              <h3>2026-05-18 · 产品视角识别切到 gpt-image-2 并重做补图参考选择</h3>
+              <span class="tag violet">API</span>
+              <span class="tag rose">UI</span>
+              <span class="tag cyan">Workflow</span>
+            </header>
+            <div class="body">
+              <p><strong>问题：</strong>同一产品素材池的视角标注仍显示通用 Vision 模型；缺角度补图固定拿第一张产品图作为参考，少侧面或内侧时容易用错误视角硬推，生成结果偏离产品真实结构。</p>
+              <p><strong>改动：</strong><code>api/main.py</code> 新增 <code>PRODUCT_VIEW_MODEL=gpt-image-2</code>，<code>analyze_product_view</code> / <code>analyze_product_views_batch</code> 改用该模型并在 <code>/health</code> 返回 <code>models.product_view</code>。<code>generateProductAngleAsset</code> 前端请求新增 <code>source_refs</code> 和 <code>source_notes</code>；<code>AudioStoryboardPlanPanel</code> 按目标视角给产品图打分，优先真实上传图、相邻视角、用途标签、置信度和低风险图，最多传 6 张。后端把多张参考图拼成同产品参考板，再用 <code>gpt-image-2</code> 生成目标角度，避免只照抄第一张。</p>
+              <p><strong>影响：</strong><code>api/main.py</code>、<code>web/lib/api.ts</code>、<code>web/components/ad-recreation-board.tsx</code>、<code>RULES.md</code>、<code>api/.env.example</code>、<code>deploy/.env.production.example</code>、<code>docs/source-analysis.html</code>。后续补产品角度必须从同一产品素材池里挑多张证据图，不要再默认第一张。</p>
+            </div>
+          </article>
          <article class="change">
            <header>
              <h3>2026-05-18 · gpt-image-2 图生图改用 edits 并处理上游饱和</h3>
--- a/web/components/ad-recreation-board.tsx
+++ b/web/components/ad-recreation-board.tsx
@@ -368,10 +368,10 @@ function audioModelTrace(models?: RuntimeModels): ModelTraceSpec {
 function productModelTrace(models?: RuntimeModels): ModelTraceSpec {
  return {
    title: "产品视角识别 / 补图",
-    model: modelList([models?.vision, models?.image]),
+    model: modelList([models?.product_view, models?.image]),
    chain: [
-      `批量视角识别：${modelValue(models?.vision)} 一次读取同一产品多张图，标注视角、左右、上下、用途和风险`,
-      `缺角度补图：${imageModelChain(models)} 按同一肩颈按摩仪结构补齐缺失视角`,
+      `批量视角识别：${modelValue(models?.product_view)} 一次读取同一产品多张图，标注视角、左右、上下、用途和风险`,
+      `缺角度补图：${imageModelChain(models)} 读取最相关的多张已上传参考图，按同一肩颈按摩仪结构补齐缺失视角`,
      "前端只保存标注和 AI 补图结果；后续生成视频时每条最多挑 6 张相关产品图",
    ],
    note: "上传产品图、重新识别、缺视角重试都会使用这组模型链路。",
@@ -620,6 +620,55 @@ function createProductRefItem(
  }
 }

+const PRODUCT_ANGLE_REFERENCE_PRIORITY: Record<string, string[]> = {
+  front: ["front", "left_45", "right_45", "side_thickness", "inner_contacts", "back_bottom"],
+  left_45: ["left_45", "front", "side_thickness", "right_45", "inner_contacts", "back_bottom"],
+  right_45: ["right_45", "front", "side_thickness", "left_45", "inner_contacts", "back_bottom"],
+  side_thickness: ["side_thickness", "left_45", "right_45", "front", "inner_contacts", "back_bottom"],
+  inner_contacts: ["inner_contacts", "side_thickness", "front", "left_45", "right_45", "back_bottom"],
+  back_bottom: ["back_bottom", "side_thickness", "inner_contacts", "left_45", "right_45", "front"],
+}
+
+function productAngleReferenceScore(item: ProductRefItem, targetView: string) {
+  const priority = PRODUCT_ANGLE_REFERENCE_PRIORITY[targetView] ?? PRODUCT_VIEW_SLOTS.map((slot) => slot.value)
+  const rank = priority.indexOf(item.view)
+  let score = rank === -1 ? 0 : 90 - rank * 12
+  if (item.source === "upload" || item.source === "library") score += 28
+  if (item.source === "ai") score -= 18
+  if (item.confidence) score += Math.round(item.confidence * 14)
+  if (item.useTags.includes("asymmetry")) score += 8
+  if (targetView === "side_thickness" && item.useTags.includes("side_thickness")) score += 16
+  if (targetView === "inner_contacts" && item.useTags.includes("inner_contact")) score += 16
+  if (targetView === "back_bottom" && item.useTags.includes("back_bottom")) score += 16
+  if (item.risk) score -= 10
+  return score
+}
+
+function selectProductAngleReferenceItems(items: ProductRefItem[], targetView: string) {
+  const unique = new Map<string, ProductRefItem>()
+  for (const item of items) {
+    if (!unique.has(item.id)) unique.set(item.id, item)
+  }
+  return [...unique.values()]
+    .sort((a, b) => productAngleReferenceScore(b, targetView) - productAngleReferenceScore(a, targetView))
+    .slice(0, 6)
+}
+
+function productAngleSourceNotes(items: ProductRefItem[]) {
+  return items.map((item, index) => {
+    const parts = [
+      `ref${index + 1}`,
+      `view=${productViewLabel(item.view)}`,
+      `source=${item.source}`,
+      item.note ? `note=${item.note}` : "",
+      formatProductOrientation(item.orientation),
+      item.landmarks?.length ? `landmarks=${item.landmarks.join("/")}` : "",
+      item.risk ? `risk=${item.risk}` : "",
+    ].filter(Boolean)
+    return parts.join("；")
+  })
+}
+
 function normalizeStoredProductItem(item: ProductRefItem, index: number): ProductRefItem {
  const ref = { ...item.ref, asset_meta: item.ref.asset_meta ?? item.assetMeta }
  const restored = createProductRefItem(
@@ -1960,10 +2009,13 @@ function AudioStoryboardPlanPanel({
    for (const slot of missing) {
      setProductAngleBusy(slot.value)
      try {
+        const references = selectProductAngleReferenceItems(working, slot.value)
        const ref = await generateProductAngleAsset(job.id, {
-          source_ref: working[0].ref,
+          source_ref: references[0].ref,
+          source_refs: references.map((item) => item.ref),
+          source_notes: productAngleSourceNotes(references),
          target_view: slot.label,
-          note: slot.hint,
+          note: `${slot.hint}；请综合这些同产品参考图补目标视角，不要只照抄某一张。`,
        })
        working = [
          ...working,
@@ -2117,13 +2169,15 @@ function AudioStoryboardPlanPanel({

  const generateMissingProductAngle = async (slot: typeof PRODUCT_VIEW_SLOTS[number]) => {
    if (!job || !productItems.length) return
-    const source = productItems[0]
+    const references = selectProductAngleReferenceItems(productItems, slot.value)
    setProductAngleBusy(slot.value)
    try {
      const ref = await generateProductAngleAsset(job.id, {
-        source_ref: source.ref,
+        source_ref: references[0].ref,
+        source_refs: references.map((item) => item.ref),
+        source_notes: productAngleSourceNotes(references),
        target_view: slot.label,
-        note: slot.hint,
+        note: `${slot.hint}；请综合这些同产品参考图补目标视角，不要只照抄某一张。`,
      })
      setProductItems((prev) => {
        const next = [...prev, createProductRefItem(ref, prev.length, "ai", slot.value, `AI 补齐：${slot.hint}`, "white", undefined, undefined, undefined, "", 1)]
--- a/web/lib/api.ts
+++ b/web/lib/api.ts
@@ -153,6 +153,7 @@ export interface RuntimeModels {
  rewrite?: string
  audio_rewrite?: string
  vision?: string
+  product_view?: string
  image?: string
  image_base_url?: string
  image_fallbacks?: string[]
@@ -224,7 +225,7 @@ export async function uploadStoryboardAsset(jobId: string, file: File): Promise<

 export async function generateProductAngleAsset(
  jobId: string,
-  body: { source_ref: ImageRef; target_view: string; note?: string },
+  body: { source_ref: ImageRef; source_refs?: ImageRef[]; source_notes?: string[]; target_view: string; note?: string },
 ): Promise<ImageRef> {
  const res = await fetch(`${API_BASE}/jobs/${jobId}/assets/product-angle`, {
    method: "POST",